Prognostic significance of MEOX2 in gliomas

The Potential Significance of the EMILIN3 Gene in Augmenting the Aggressiveness of Low-Grade Gliomas is Noteworthy

Purpose

Low-grade gliomas (LGG) are common brain tumors with high mortality rates. Cancer cell invasion is a significant factor in tumor metastasis. Novel biomarkers are urgently needed to predict LGG prognosis effectively.

Methods

The data for LGG were obtained from the Bioinformatics database. A consensus clustering analysis was performed to identify molecular subtypes linked with invasion in LGG. Differential expression analysis was performed to identify differentially expressed genes (DEGs) between the identified clusters. Enrichment analyses were then conducted to explore the function for DEGs. Prognostic signatures were placed, and their predictive power was assessed. Furthermore, the invasion-related prognostic signature was validated using the CGGA dataset. Subsequently, clinical specimens were procured in order to validate the expression levels of the distinct genes examined in this research, and to further explore the impact of these genes on the glioma cell line LN229 and HS-683.

Results

Two invasion-related molecular subtypes of LGG were identified, and we sifted 163 DEGs between them. The enrichment analyses indicated that DEGs are mainly related to pattern specification process. Subsequently, 10 signature genes (IGF2BP2, SRY, CHI3L1, IGF2BP3, MEOX2, ABCC3, HOXC4, OTP, METTL7B, and EMILIN3) were sifted out to construct a risk model. Besides, the survival (OS) in the high-risk group was lower. The performance of the risk model was verified. Furthermore, a highly reliable nomogram was generated. Cellular experiments revealed the ability to promote cell viability, value-addedness, migratory ability, invasive ability, and colony-forming ability of the glioma cell line LN229 and HS-683. The qRT-PCR analysis of clinical glioma samples showed that these 10 genes were expressed at higher levels in high-grade gliomas than in low-grade gliomas, suggesting that these genes are associated with poor prognosis of gliomas.

Conclusion

Our study sifted out ten invasion-related biomarkers of LGG, providing a reference for treatments and prognostic prediction in LGG.

Prognostic relevance and validation of ARPC1A in the progression of low-grade glioma

Low-grade glioma (LGG) is a grade II-III glioma accompanied by distinct clinical and molecular characteristics and the studies related to its prognosis are still unclear. The objective of this study is to explore the involvement of mitochondrial-related genes SLBP, COMMD7, LSM4, TOMM34, RPP40, FKBP1A, ARPC1A, and TBCA for the prognosis of LGG. We detected differences in the expression of some of the genes by analyzing the bioinformatics dataset and combining it with RT-PCR experiments. Subsequently, a nomogram was constructed and validated for the clinical relevance of risk factors such as age, WHO grade, IDH mutation status, Ch.1p19q co-deletion status, and high and low expression of ARPC1A to predict the 1-, 3-, 5-year overall survival and prognostic relevance of ARPC1A. Gene set enrichment analysis was performed for the relevant datasets pertinent to the expression of ARPC1A to elucidate the cancer-promoting pathways involved in the LGG through KEGG and GO analysis. Transfection assays, CCK-8 assays, and flow cytometry were used to determine the proliferation rate, and apoptosis rate of the HS683 and SW1783 cell lines respectively. Western blotting was used to examine the involvement of the cancer-promoting activity of ARPC1A through MAPK signaling. In this study, the prognostic value of ARPC1A in LGG was found by bioinformatics analysis combined with experimental approach analysis and may be a significant independent risk factor. ARPC1A fosters a higher LGG proliferation rate that may control the MAP kinase signaling and could be a prominent biomarker for LGG. Future studies are warranted to explore its clinical implications.

DNA methylation landscapes in DIPG reveal methylome variability that can be modified pharmacologically

Background

Diffuse intrinsic pontine glioma (DIPG) is a uniformly lethal brainstem tumor of childhood, driven by histone H3 K27M mutation and resultant epigenetic dysregulation. Epigenomic analyses of DIPG have shown global loss of repressive chromatin marks accompanied by DNA hypomethylation. However, studies providing a static view of the epigenome do not adequately capture the regulatory underpinnings of DIPG cellular heterogeneity and plasticity.

Methods

To address this, we performed whole-genome bisulfite sequencing on a large panel of primary DIPG specimens and applied a novel framework for analysis of DNA methylation variability, permitting the derivation of comprehensive genome-wide DNA methylation potential energy landscapes that capture intrinsic epigenetic variation.

Results

We show that DIPG has a markedly disordered epigenome with increasingly stochastic DNA methylation at genes regulating pluripotency and developmental identity, potentially enabling cells to sample diverse transcriptional programs and differentiation states. The DIPG epigenetic landscape was responsive to treatment with the hypomethylating agent decitabine, which produced genome-wide demethylation and reduced the stochasticity of DNA methylation at active enhancers and bivalent promoters. Decitabine treatment elicited changes in gene expression, including upregulation of immune signaling such as the interferon response, STING, and MHC class I expression, and sensitized cells to the effects of histone deacetylase inhibition.

Conclusions

This study provides a resource for understanding the epigenetic instability that underlies DIPG heterogeneity. It suggests the application of epigenetic therapies to constrain the range of epigenetic states available to DIPG cells, as well as the use of decitabine in priming for immune-based therapies.

Widespread 8-oxoguanine modifications of miRNA seeds differentially regulate redox-dependent cancer development

Oxidative stress contributes to tumourigenesis by altering gene expression. One accompanying modification, 8-oxoguanine (o8G) can change RNA-RNA interactions via o8G•A base pairing, but its regulatory roles remain elusive. Here, on the basis of o8G-induced guanine-to-thymine (o8G > T) variations featured in sequencing, we discovered widespread position-specific o8Gs in tumour microRNAs, preferentially oxidized towards 5' end seed regions (positions 2-8) with clustered sequence patterns and clinically associated with patients in lower-grade gliomas and liver hepatocellular carcinoma. We validated that o8G at position 4 of miR-124 (4o8G-miR-124) and 4o8G-let-7 suppress lower-grade gliomas, whereas 3o8G-miR-122 and 4o8G-let-7 promote malignancy of liver hepatocellular carcinoma by redirecting the target transcriptome to oncogenic regulatory pathways. Stepwise oxidation from tumour-promoting 3o8G-miR-122 to tumour-suppressing 2,3o8G-miR-122 occurs and its specific modulation in mouse liver effectively attenuates diethylnitrosamine-induced hepatocarcinogenesis. These findings provide resources and insights into epitranscriptional o8G regulation of microRNA functions, reprogrammed by redox changes, implicating its control for cancer treatment.

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma

Background

Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms.

Methods

Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets.

Results

We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors.

Conclusion

In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.

Diagnostic Utility of Immunohistochemical Detection of MEOX2, SOX11, INSM1 and EGFR in Gliomas

Histological identification of dispersed glioma cells in small biopsies can be challenging, especially in tumours lacking the IDH1 R132H mutation or alterations in TP53. We postulated that immunohistochemical detection of proteins expressed preferentially in gliomas (EGFR, MEOX2, CD34) or during embryonal development (SOX11, INSM1) can be used to distinguish reactive gliosis from glioma. Tissue microarrays of 46 reactive glioses, 81 glioblastomas, 34 IDH1-mutant diffuse gliomas, and 23 gliomas of other types were analysed. Glial neoplasms were significantly more often (p < 0.001, χ2) positive for EGFR (34.1% vs. 0%), MEOX2 (49.3% vs. 2.3%), SOX11 (70.5% vs. 20.4%), and INSM1 (65.4% vs. 2.3%). In 94.3% (66/70) of the glioblastomas, the expression of at least two markers was observed, while no reactive gliosis showed coexpression of any of the proteins. Compared to IDH1-mutant tumours, glioblastomas showed significantly higher expression of EGFR, MEOX2, and CD34 and significantly lower positivity for SOX11. Non-diffuse gliomas were only rarely positive for any of the five markers tested. Our results indicate that immunohistochemical detection of EGFR, MEOX2, SOX11, and INSM1 can be useful for detection of glioblastoma cells in limited histological samples, especially when used in combination.

The PANoptosis-related signature indicates the prognosis and tumor immune infiltration features of gliomas

Background

Gliomas are the most common primary tumors of the central nervous system, with high heterogeneity and highly variable survival rates. Accurate classification and prognostic assessment are key to the selection of treatment strategies. One hallmark of the tumor is resistance to cell death. PANoptosis, a novel mode of programmed cell death, has been frequently reported to be involved in the innate immunity associated with pathogen infection and played an important role in cancers. However, the intrinsic association of PANoptosis with glioma requires deeper investigation.

Methods

The genetics and expression of the 17 reported PANoptosome-related genes were analyzed in glioma. Based on these genes, patients were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between clusters, a prognostic model called PANopotic score was constructed after univariate Cox regression, LASSO regression, and multivariate Cox regression. The expression of the 5 genes included in the PANopotic score was also examined by qPCR in our cohort. The prognostic differences, clinical features, TME infiltration status, and immune characteristics between PANoptotic clusters and score groups were compared, some of which even extended to pan-cancer levels.

Results

Gene mutations, CNVs and altered gene expression of PANoptosome-related genes exist in gliomas. Two PANoptotic clusters were significantly different in prognosis, clinical features, immune characteristics, and mutation landscapes. The 5 genes included in the PANopotic score had significantly altered expression in glioma samples in our cohort. The high PANoptotic score group was inclined to show an unfavorable prognosis, lower tumor purity, worse molecular genetic signature, and distinct immune characteristics related to immunotherapy. The PANoptotic score was considered as an independent prognostic factor for glioma and showed superior prognostic assessment efficacy over several reported models. PANopotic score was included in the nomogram constructed for the potential clinical prognostic application. The associations of PANoptotic score with prognostic assessment and tumor immune characteristics were also reflected at the pan-cancer level.

Conclusion

Molecular subtypes of glioma based on PANoptosome-related genes were proposed and PANoptotic score was constructed with different clinical characteristics of anti-tumor immunity. The potential intrinsic association between PANoptosis and glioma subtypes, prognosis, and immunotherapy was revealed.

Modeling brain and neural crest neoplasms with human pluripotent stem cells

Pluripotent stem cells offer unique avenues to study human-specific aspects of disease and are a highly versatile tool in cancer research. Oncogenic processes and developmental programs often share overlapping transcriptomic and epigenetic signatures, which can be reactivated in induced pluripotent stem cells. With the emergence of brain organoids, the ability to recapitulate brain development and structure has vastly improved, making in vitro models more realistic and hence more suitable for biomedical modeling. This review highlights recent research and current challenges in human pluripotent stem cell modeling of brain and neural crest neoplasms, and concludes with a call for more rigorous quality control and for the development of models for rare tumor subtypes.

RING Finger Protein 10 Regulates AP-1/Meox2 to Mediate Pirarubicin-Induced Cardiomyocyte Apoptosis

Pirarubicin (THP) is one of the classic chemotherapy drugs for cancer treatment. It is often clinically limited because of its cardiotoxicity. The occurrence and development of THP-mediated chemotherapy-related cardiotoxicity (CRC) may be reversed by RING finger protein 10 (RNF10). This study was performed with the aim of evaluating the inhibitory effect of RNF10 on THP-mediated CRC and its molecular mechanism. In vivo, we found that the expression of RNF10 decreased in THP-induced CRC rats, accompanied by Meox2 inhibition and AP-1 activation, resulting in increased cardiomyocyte apoptosis. After small interfering RNA (siRNA) and lentivirus transfection (Lv) of RNF10 in vitro, the expression of RNF10, Meox2, and AP-1 proteins and the degree of cardiomyocyte apoptosis were detected. We found that overexpression of RNF10 in H9C2 cardiomyocytes significantly promoted Meox2 and inhibited AP-1, alleviated apoptosis, and showed further inhibitory activity on THP-induced cardiomyocyte toxicity. Silencing RNF10 showed the opposite result. Our study showed that RNF10 inhibited THP-induced CRC through the activity of Meox2 and AP-1 proteins. RNF10 may be the next drug target for the treatment of CRC and other related cardiovascular diseases.

Low expression of MEOX2 is associated with poor survival in patients with breast cancer

Aim: To investigate associations of MEOX2 expression with clinicopathological features and survival of breast cancer patients. Materials & methods: We used a breast cancer tissue microarray for immunohistochemistry. Associations between MEOX2 expression and clinicopathological features were analyzed using the χ-square test. Survival analysis was determined using a Kaplan-Meier curve. Multivariate Cox regression was used to determine associations of MEOX2 expression with overall survival. Results: We found that 74.1% of patients (100/135) had expression of MEOX2 at varying levels. MEOX2 was associated with histological grade and negatively correlated with Ki67 expression. Lower MEOX2 expression was significantly associated with decreased overall survival (p = 0.0011). Conclusion: MEOX2 expression could be a novel diagnostic and prognostic biomarker of breast cancer.

ABI2-mediated MEOX2/KLF4-NANOG axis promotes liver cancer stem cell and drives tumour recurrence

Tumour recurrence and drug resistance in hepatocellular carcinoma remain challenging. Cancer stem cells (CSCs) are responsible for tumour initiation because of their stemness characteristics. CSCs accounting for drug resistance and tumour relapse are promising therapeutic targets. We report that Abelson interactor 2 (ABI2) is a novel therapeutic target of HCC CSCs. First, ABI2 was upregulated in HCC tissues compared with liver tissues and was associated with tumour size, pathological grade, liver cirrhosis, worse prognosis and a high recurrence rate. Functional studies illustrate that ABI2 knockdown suppresses cell growth, migration, invasion and sorafenib resistance in vitro. Furthermore, ABI2 knockdown inhibited HCC sphere formation and decreased the CD24⁺ , CD133⁺ and CD326⁺ CSCs populations, suggesting the suppression of HCC stemness characteristics. A tumour xenograft model and limiting dilution assay demonstrated the inhibition of tumorigenicity and tumour initiation. Moreover, molecular mechanism studies showed that ABI2 recruits and directly interacts with the transcription factor MEOX2, which binds to the KLF4 and NANOG promoter regions to activate their transcription. Furthermore, overexpression of MEOX2 restored HCC malignant behaviour and the CSC population. The ABI2-mediated transcriptional axis MEOX2/KLF4-NANOG promotes HCC growth, metastasis and sorafenib resistance by maintaining the CSC population, suggesting that ABI2 is a promising CSC target in HCC treatment.

A single-cell based precision medicine approach using glioblastoma patient-specific models

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting.

High expression of N-type calcium channel indicates a favorable prognosis in gliomas

For the diagnosis and prognosis of glioma, the development of prognostic biomarkers is critical. The N-type calcium channel, whose predominant subunit is encoded by calcium voltage-gated channel subunit alpha1 B (CACNA1B), is mostly found in the nervous system and is closely associated with neurosensory functions. However, the link between the expression of CACNA1B and glioma remains unknown. We used ONCOMINE to explore the differences in CACNA1B expression among different cancers. We then conducted survival analysis and COX analysis using TCGA_LGG and TCGA_GBM datasets, which were divided into CACNA1Bhigh and CACNA1Blow based on the median. We examined the differences in other favorable prognostic markers or clinical characteristics between CACNA1Bhigh and CACNA1Blow using t tests. Differentially expressed genes were identified, and KEGG pathway enrichment was performed. We compared the expression of methyltransferases and analyzed the differentially methylated regions. Immunohistochemistry results were retrieved from the Human Protein Atlas database for validation purposes. CACNA1B was expressed at lower levels in gliomas, and, for the first time, we found that high expression of CACNA1B in gliomas predicts a good prognosis. Other favorable prognostic markers, such as isocitrate dehydrogenase mutation, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase promoter methylation, were increased in tandem with high expression of CACNA1B. Differentially expressed genes were enriched in multiple pathways related to cancer progression and aberrant epigenetic alterations were significantly associated with CACNA1B. High expression of N-type calcium channels indicates a favorable prognosis for gliomas. This study provides a better understanding of the link between gliomas and N-type calcium channels and may offer guidance for the future treatment of gliomas.

A Novel Multi-Omics Analysis Model for Diagnosis and Survival Prediction of Lower-Grade Glioma Patients

Background

Lower-grade gliomas (LGGs) are characterized by remarkable genetic heterogeneity and different clinical outcomes. Classification of LGGs is improved by the development of molecular stratification markers including IDH mutation and 1p/19q chromosomal integrity, which are used as a hallmark of survival and therapy sensitivity of LGG patients. However, the reproducibility and sensitivity of the current classification remain ambiguous. This study aimed to construct more accurate risk-stratification approaches.

Methods

According to bioinformatics, the sequencing profiles of methylation and transcription and imaging data derived from LGG patients were analyzed and developed predictable risk score and radiomics score. Moreover, the performance of predictable models was further validated.

Results

In this study, we determined a cluster of 6 genes that were correlated with IDH mutation/1p19q co-deletion status. Risk score model was calculated based on 6 genes and showed gratifying sensitivity and specificity for survival prediction and therapy response of LGG patients. Furthermore, a radiomics risk score model was established to noninvasively assist judgment of risk score in pre-surgery. Taken together, a predictable nomogram that combined transcriptional signatures and clinical characteristics was established and validated to be preferable to the histopathological classification. Our novel multi-omics nomograms showed a satisfying performance. To establish a user-friendly application, the nomogram was further developed into a web-based platform: https://drw576223193.shinyapps.io/Nomo/, which could be used as a supporting method in addition to the current histopathological-based classification of gliomas.

Conclusions

Our novel multi-omics nomograms showed the satisfying performance of LGG patients and assisted clinicians to draw up individualized clinical management.

MEOX2-mediated regulation of Cathepsin S promotes cell proliferation and motility in glioma

Nuclear transcription factor Mesenchyme Homeobox 2 (MEOX2) is a homeobox gene that is originally discovered to suppress the growth of vascular smooth muscle and endothelial cells. However, whether or not it is connected to cancer is yet unknown. Here, we report that MEOX2 functions as a tumor-initiating element in glioma. Bioinformatic analyses of public databases and investigation of MEOX2 expression in patients with glioma demonstrated that MEOX2 was abundant at both mRNA and protein levels in glioma. MEOX2 expression was shown to be inversely linked with the prognosis of glioma patients. MEOX2 inhibition changed the morphology of glioma cells, inhibited cell proliferation and motility, whereas had no effect on cell apoptosis. Besides, silencing MEOX2 also hampered the epithelial-mesenchymal transition (EMT), focal adhesion formation, and F-actin assembly. Overexpression of MEOX2 exhibited opposite effects. Importantly, RNA-sequencing, ChIP-qPCR assay, and luciferase reporter assay revealed Cathepsin S (CTSS) as a novel transcriptional target of MEOX2 in glioma cells. Consistently, MEOX2 causes glioma tumor development in mice and greatly lowers the survival period of tumor-bearing mice. Our findings indicate that MEOX2 promotes tumorigenesis and progression of glioma partially through the regulation of CTSS. Targeting MEOX2-CTSS axis might be a promising alternative for the treatment of glioma.

Circular RNA circBFAR promotes glioblastoma progression by regulating a miR-548b/FoxM1 axis

Glioblastoma multiforme (GBM) is the most common and aggressive type of tumor of the primary nervous system. Treatment options for GBM include surgery, chemotherapy, and radiation therapy; however, the clinical outcomes are poor, with a high rate of recurrence. An increasing number of studies have shown that circular RNAs (circRNAs) serve important roles in several types of cancer. Gene Expression Omnibus (GEO) database was utilized to identify the differentially expressed circRNAs and their biological functions. Then, we detected the circular RNA bifunctional apoptosis regulator (circBFAR) was significantly increased in three GEO datasets. However, the role of circBFAR has not been reported in GBM. In this study, the expression of circBFAR was significantly increased both in GBM tissues or cell lines and was negatively correlated with overall survival in patients with GBM. Knockdown of circBFAR inhibited proliferation and invasion both in vitro and in vivo. Increased expression of circBFAR resulted in a reduction of miR-548b expression in glioma cells. A luciferase reporter and RIP assay indicated that miR-548b was a direct target of circBFAR, and miR-548b may negatively regulate the expression of FoxM1. Rescue experiments showed that overexpression of FoxM1 could counter the effect of circBFAR silencing on the proliferation and invasion of glioma cell lines. Moreover, we identified that circBFAR regulates FoxM1 by interacting with miR-548b in glioma cells. In conclusion, the present study demonstrated that a circBFAR/miR-548b/FoxM1 axis regulates the development of GBM and highlights potentially novel therapeutic targets for the treatment of GBM.

OUP accepted manuscript

BACKGROUND

Glioblastoma (GBM) is an aggressive tumor that frequently exhibits gain of chromosome 7, loss of chromosome 10, and aberrantly activated receptor tyrosine kinase signaling pathways. Previously, we identified Mesenchyme Homeobox 2 (MEOX2), a gene located on chromosome 7, as an upregulated transcription factor in GBM. Overexpressed transcription factors can be involved in driving GBM. Here, we aimed to address the role of MEOX2 in GBM.

METHODS

Patient-derived GBM tumorspheres were used to constitutively knockdown or overexpress MEOX2 and subjected to in vitro assays including western blot to assess ERK phosphorylation. Cerebral organoid models were used to investigate the role of MEOX2 in growth initiation. Intracranial mouse implantation models were used to assess the tumorigenic potential of MEOX2. RNA-sequencing, ACT-seq, and CUT&amp;Tag were used to identify MEOX2 target genes.

RESULTS

MEOX2 enhanced ERK signaling through a feed-forward mechanism. We identified Ser155 as a putative ERK-dependent phosphorylation site upstream of the homeobox-domain of MEOX2. S155A substitution had a major effect on MEOX2 protein levels and altered its subnuclear localization. MEOX2 overexpression cooperated with p53 and PTEN loss in cerebral organoid models of human malignant gliomas to induce cell proliferation. Using high-throughput genomics, we identified putative transcriptional target genes of MEOX2 in patient-derived GBM tumorsphere models and a fresh frozen GBM tumor.

CONCLUSIONS

We identified MEOX2 as an oncogenic transcription regulator in GBM. MEOX2 increases proliferation in cerebral organoid models of GBM and feeds into ERK signaling that represents a core signaling pathway in GBM.

MEOX2 Transcription Factor Is Involved in Survival and Adhesion of Glioma Stem-like Cells

Simple Summary

Glioblastoma is the most common and lethal primary brain tumor for which no curative treatment currently exists. In our previous work, we showed that MEOX2 was associated with a poor patient prognosis but its biological involvement in tumor development remains ill defined. To this purpose, the aim of our study was to investigate the role of MEOX2 in patient-derived glioblastoma cell cultures. We unraveled the MEOX2 contribution to cell viability and growth and its potential involvement in phenotype and adhesion properties of glioblastoma cells. This work paves the way toward a better understanding of the role of MEOX2 in the pathophysiology of primary brain tumors.

Abstract

The high expression of MEOX2 transcription factor is closely associated with poor overall survival in glioma. MEOX2 has recently been described as an interesting prognostic biomarker, especially for lower grade glioma. MEOX2 has never been studied in glioma stem-like cells (GSC), responsible for glioma recurrence. The aim of our study was to investigate the role of MEOX2 in GSC. Loss of function approach using siRNA was used to assess the impact of MEOX2 on GSC viability and stemness phenotype. MEOX2 was localized in the nucleus and its expression was heterogeneous between GSCs. MEOX2 expression depends on the methylation state of its promoter and is strongly associated with IDH mutations. MEOX2 is involved in cell proliferation and viability regulation through ERK/MAPK and PI3K/AKT pathways. MEOX2 loss of function correlated with GSC differentiation and acquisition of neuronal lineage characteristics. Besides, inhibition of MEOX2 is correlated with increased expression of CDH10 and decreased pFAK. In this study, we unraveled, for the first time, MEOX2 contribution to cell viability and proliferation through AKT/ERK pathway and its potential involvement in phenotype and adhesion properties of GSC.

The long non-coding RNA HOTAIRM1 promotes tumor aggressiveness and radiotherapy resistance in glioblastoma

Glioblastoma is the most common malignant primary brain tumor. To date, clinically relevant biomarkers are restricted to isocitrate dehydrogenase (IDH) gene 1 or 2 mutations and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. Long non-coding RNAs (lncRNAs) have been shown to contribute to glioblastoma pathogenesis and could potentially serve as novel biomarkers. The clinical significance of HOXA Transcript Antisense RNA, Myeloid-Specific 1 (HOTAIRM1) was determined by analyzing HOTAIRM1 in multiple glioblastoma gene expression data sets for associations with prognosis, as well as, IDH mutation and MGMT promoter methylation status. Finally, the role of HOTAIRM1 in glioblastoma biology and radiotherapy resistance was characterized in vitro and in vivo. We identified HOTAIRM1 as a candidate lncRNA whose up-regulation is significantly associated with shorter survival of glioblastoma patients, independent from IDH mutation and MGMT promoter methylation. Glioblastoma cell line models uniformly showed reduced cell viability, decreased invasive growth and diminished colony formation capacity upon HOTAIRM1 down-regulation. Integrated proteogenomic analyses revealed impaired mitochondrial function and determination of reactive oxygen species (ROS) levels confirmed increased ROS levels upon HOTAIRM1 knock-down. HOTAIRM1 knock-down decreased expression of transglutaminase 2 (TGM2), a candidate protein implicated in mitochondrial function, and knock-down of TGM2 mimicked the phenotype of HOTAIRM1 down-regulation in glioblastoma cells. Moreover, HOTAIRM1 modulates radiosensitivity of glioblastoma cells both in vitro and in vivo. Our data support a role for HOTAIRM1 as a driver of biological aggressiveness, radioresistance and poor outcome in glioblastoma. Targeting HOTAIRM1 may be a promising new therapeutic approach.

Differential and Prognostic Significance of HOXB7 in Gliomas

Diffuse glioma is the most common primary tumor of the central nervous system. The prognosis of the individual tumor is heavily dependent on its grade and subtype. Homeobox B7 (HOXB7), a member of the homeobox family, is abnormally overexpressed in a variety of tumors. However, its function in glioma is unclear. In this study, HOXB7 mRNA and protein expression levels were analyzed in 401 gliomas from the CGGA RNA-seq database (325 cases) and our hospital (76 cases). HOXB7 expression, at both mRNA and protein levels, were upregulated in glioblastoma (GBM) and isocitrate dehydrogenase 1 (IDH1) wild-type glioma tissues. Kaplan–Meier with log-rank test showed that patients with high HOXB7 expression had a poor prognosis (p < 0.0001). Moreover, HOXB7 protein was deleted in 90.9% (20/22) of oligodendrogliomas and 13.0% (3/23) of astrocytomas. The sensitivity and specificity of HOXB7 protein deletion in oligodendroglioma were 90.9% (20/22) and 87.0% (20/23), respectively. To verify the reliability of using HOXB7 in differentiating oligodendroglioma, we used 1p/19q fluorescence in situ hybridization (FISH) testing as a positive control. The Cohen’s kappa coefficient of HOXB7 immunohistochemistry staining and 1p/19q FISH testing was 0.778 (95% CI: 0.594–0.962, p < 0.001). In conclusion, HOXB7 is an independent predictor of poor prognosis in all grade gliomas. Additionally, HOXB7 is also a highly sensitive and specific indicator to differentiate oligodendroglioma from astrocytoma.

Multi-project and Multi-profile joint Non-negative Matrix Factorization for cancer omic datasets

MOTIVATION

The integration of multi-omic data using machine learning methods has been focused on solving relevant tasks such as predicting sensitivity to a drug or subtyping patients. Recent integration methods, such as joint Non-negative Matrix Factorization (jNMF), have allowed researchers to exploit the information in the data to unravel the biological processes of multi-omic datasets.

RESULTS

We present a novel method called Multi-project and Multi-profile joint Non-negative Matrix Factorization (M&M-jNMF) capable of integrating data from different sources, such as experimental and observational multi-omic data. The method can generate co-clusters between observations, predict profiles and relate latent variables. We applied the method to integrate low-grade glioma omic profiles from The Cancer Genome Atlas (TCGA) and Cell Line Encyclopedia (CCLE) projects. The method allowed us to find gene clusters mainly enriched in cancer-associated terms. We identified groups of patients and cell lines similar to each other by comparing biological processes. We predicted the drug profile for patients, and we identified genetic signatures for resistant and sensitive tumors to a specific drug.

AVAILABILITY AND IMPLEMENTATION

Source code repository is publicly available at https://bitbucket.org/dsalazarb/mmjnmf/ - Zenodo DOI: 10.5281/zenodo.5150920.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients

Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients.

Knockdown of CLN5 inhibits the tumorigenic properties of glioblastoma cells via the Akt/mTOR signaling pathway

Gliomas are highly malignant tumors with a rapid progression and poor prognosis. The present study investigated the cellular effects of CLN5-knockdown in the glioblastoma (GBM) U251 and U87MG cell lines. The Cell Counting Kit-8 and colony formation assays indicated that CLN5-knockdown inhibited the proliferation of GBM cells. Additionally, the results of the Transwell and scratch assays revealed that CLN5-knockdown significantly inhibited migration and invasion, and the flow cytometry analysis confirmed that apoptosis was promoted. Knockdown of CLN5 downregulated the expression levels of MMP-2, Bcl-2, cyclin D1, CDK4 and CDK6, and upregulated the expression levels of Bax and activated caspase-9. Additionally, it blocked GBM cells in the G1-phase and induced early apoptosis. Knockdown of CLN5 inhibited the activation of the Akt and mTOR signaling pathways in GBM by decreasing the levels of phosphorylated (p)-Akt and p-mTOR. The present data suggested that downregulation of CLN5 may be a potential treatment option for GBM. Knockdown of CLN5 inhibited the development of GBM via the inhibition of the Akt and mTOR signaling pathways.

A Candidate Prognostic Biomarker Complement Factor I Promotes Malignant Progression in Glioma

Objectives: Glioma is the most common and aggressive type of primary central nervous system (CNS) tumor in adults and is associated with substantial mortality rates. The aim of our study was to evaluate the prognostic significance and function of the complement factor I (CFI) in glioma.

Materials and Methods: The expression levels of CFI in glioma tissues and the survival of the CFI^high and CFI^low patient groups were analyzed using The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). The correlation between CFI expression and clinicopathological features of glioma was determined by univariate and multivariate Cox regression analyses in the Chinese Glioma Genome Atlas (CGGA) database. The functional role of CFI in glioma was established through routine in vitro and in vivo assays.

Results: CFI is overexpressed in glioma and its high levels correlated with poor outcomes in both TCGA and CGGA datasets. Furthermore, CFI was identified as an independent prognostic factor of glioma in the CGGA database. CFI knockdown in glioma cell lines inhibited growth in vitro and in vivo, whereas its ectopic expression increased glioma cell proliferation, migration, and invasion in vitro. CFI protein levels were also significantly higher in the glioma tissues resected from patients and correlated to worse prognosis.

Conclusions: CFI is a potential prognostic biomarker in glioma and drives malignant progression.

Both IDO1 and TDO contribute to the malignancy of gliomas via the Kyn-AhR-AQP4 signaling pathway

Indoleamine 2,3-dioxygenase 1 (IDO1), indoleamine 2,3-dioxygenase 2 (IDO2), and tryptophan 2,3-dioxygenase (TDO) initiate the first step of the kynurenine pathway (KP), leading to the transformation of L-tryptophan (Trp) into L-kynurenine (Kyn) and other downstream metabolites. Kyn is known as an endogenous ligand of the aryl hydrocarbon receptor (AhR). Activation of AhR through TDO-derived Kyn is a novel mechanism to support tumor growth in gliomas. However, the role of IDO1 and IDO2 in this mechanism is still unknown. Herein, by using clinical samples, we found that the expression and activity of IDO1 and/or TDO (IDO1/TDO) rather than IDO2 were positively correlated with the pathologic grades of gliomas. The expression of IDO1/TDO rather than IDO2 was positively correlated with the Ki67 index and overall survival. The expression of IDO1/TDO was positively correlated with the expression of aquaporin 4 (AQP4), implying the potential involvement of IDO1/TDO in glioma cell motility. Mechanistically, we found that IDO1/TDO accounted for the release of Kyn, which activated AhR to promote cell motility via the Kyn-AhR-AQP4 signaling pathway in U87MG glioma cells. RY103, an IDO1/TDO dual inhibitor, could block the IDO1/TDO-Kyn-AhR-AQP4 signaling pathway and exert anti-glioma effects in GL261 orthotopic glioma mice. Together, our results showed that the IDO1/TDO-Kyn-AhR-AQP4 signaling pathway is a new mechanism underlying the malignancy of gliomas, and suggest that both IDO1 and TDO might be valuable therapeutic targets for gliomas.

A Novel Prognostic Signature of Transcription Factors for the Prediction in Patients With GBM

Background: Although the diagnosis and treatment of glioblastoma (GBM) is significantly improved with recent progresses, there is still a large heterogeneity in therapeutic effects and overall survival. The aim of this study is to analyze gene expressions of transcription factors (TFs) in GBM so as to discover new tumor markers.

Methods: Differentially expressed TFs are identified by data mining using public databases. The GBM transcriptome profile is downloaded from The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) method is used to cluster the differentially expressed genes to discover hub genes and signal pathways. The TFs affecting the prognosis of GBM are screened by univariate and multivariate COX regression analysis, and the receiver operating characteristic (ROC) curve is determined. The GBM hazard model and nomogram map are constructed by integrating the clinical data. Finally, the TFs involving potential signaling pathways in GBM are screened by Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.

Results: There are 68 differentially expressed TFs in GBM, of which 43 genes are upregulated and 25 genes are downregulated. NMF clustering analysis suggested that GBM patients are divided into three groups: Clusters A, B, and C. LHX2, MEOX2, SNAI2, and ZNF22 are identified from the above differential genes by univariate/multivariate regression analysis. The risk score of those four genes are calculated based on the beta coefficient of each gene, and we found that the predictive ability of the risk score gradually increased with the prolonged predicted termination time by time-dependent ROC curve analysis. The nomogram results have showed that the integration of risk score, age, gender, chemotherapy, radiotherapy, and 1p/19q can further improve predictive ability towards the survival of GBM. The pathways in cancer, phosphoinositide 3-kinases (PI3K)–Akt signaling, Hippo signaling, and proteoglycans, are highly enriched in high-risk groups by GSEA. These genes are mainly involved in cell migration, cell adhesion, epithelial–mesenchymal transition (EMT), cell cycle, and other signaling pathways by GO and KEGG analysis.

Conclusion: The four-factor combined scoring model of LHX2, MEOX2, SNAI2, and ZNF22 can precisely predict the prognosis of patients with GBM.

Chromatin landscapes reveal developmentally encoded transcriptional states that define human glioblastoma

Mack et al. defined active chromatin landscapes of glioblastoma stem cells (GSCs) and primary tumor specimens, revealing novel transcriptional regulatory circuits and therapeutic targets. Super-enhancers identified essential transcription factors that underlie GSC identity and intertumoral diversity, potentially informing precision medicine.

Glioblastoma is an incurable brain cancer characterized by high genetic and pathological heterogeneity. Here, we mapped active chromatin landscapes with gene expression, whole exomes, copy number profiles, and DNA methylomes across 44 patient-derived glioblastoma stem cells (GSCs), 50 primary tumors, and 10 neural stem cells (NSCs) to identify essential super-enhancer (SE)–associated genes and the core transcription factors that establish SEs and maintain GSC identity. GSCs segregate into two groups dominated by distinct enhancer profiles and unique developmental core transcription factor regulatory programs. Group-specific transcription factors enforce GSC identity; they exhibit higher activity in glioblastomas versus NSCs, are associated with poor clinical outcomes, and are required for glioblastoma growth in vivo. Although transcription factors are commonly considered undruggable, group-specific enhancer regulation of the MAPK/ERK pathway predicts sensitivity to MEK inhibition. These data demonstrate that transcriptional identity can be leveraged to identify novel dependencies and therapeutic approaches.